Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations690
Missing cells0
Missing cells (%)0.0%
Duplicate rows43
Duplicate rows (%)6.2%
Total size in memory54.0 KiB
Average record size in memory80.2 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 43 (6.2%) duplicate rowsDuplicates
Bare Nuclei is highly overall correlated with Bland Chromatin and 7 other fieldsHigh correlation
Bland Chromatin is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Class is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Clump Thickness is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Marginal Adhesion is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 2 other fieldsHigh correlation
Normal Nucleoli is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Single Epithelial Cell Size is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Shape is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Size is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation

Reproduction

Analysis started2025-01-05 15:04:47.147379
Analysis finished2025-01-05 15:04:55.007542
Duration7.86 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Clump Thickness
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4289855
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:55.099986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8173782
Coefficient of variation (CV)0.63612271
Kurtosis-0.62777543
Mean4.4289855
Median Absolute Deviation (MAD)2
Skewness0.58938754
Sum3056
Variance7.9376202
MonotonicityNot monotonic
2025-01-05T16:04:55.200023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 142
20.6%
5 129
18.7%
3 105
15.2%
4 80
11.6%
10 69
10.0%
2 50
 
7.2%
8 46
 
6.7%
6 33
 
4.8%
7 23
 
3.3%
9 13
 
1.9%
ValueCountFrequency (%)
1 142
20.6%
2 50
 
7.2%
3 105
15.2%
4 80
11.6%
5 129
18.7%
6 33
 
4.8%
7 23
 
3.3%
8 46
 
6.7%
9 13
 
1.9%
10 69
10.0%
ValueCountFrequency (%)
10 69
10.0%
9 13
 
1.9%
8 46
 
6.7%
7 23
 
3.3%
6 33
 
4.8%
5 129
18.7%
4 80
11.6%
3 105
15.2%
2 50
 
7.2%
1 142
20.6%

Uniformity of Cell Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1333333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:55.297023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0424508
Coefficient of variation (CV)0.97099494
Kurtosis0.10094757
Mean3.1333333
Median Absolute Deviation (MAD)0
Skewness1.2311166
Sum2162
Variance9.256507
MonotonicityNot monotonic
2025-01-05T16:04:55.392968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 378
54.8%
10 65
 
9.4%
3 51
 
7.4%
2 45
 
6.5%
4 40
 
5.8%
5 30
 
4.3%
8 29
 
4.2%
6 27
 
3.9%
7 19
 
2.8%
9 6
 
0.9%
ValueCountFrequency (%)
1 378
54.8%
2 45
 
6.5%
3 51
 
7.4%
4 40
 
5.8%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.8%
8 29
 
4.2%
9 6
 
0.9%
10 65
 
9.4%
ValueCountFrequency (%)
10 65
 
9.4%
9 6
 
0.9%
8 29
 
4.2%
7 19
 
2.8%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.8%
3 51
 
7.4%
2 45
 
6.5%
1 378
54.8%

Uniformity of Cell Shape
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2043478
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:55.489399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9608444
Coefficient of variation (CV)0.92400842
Kurtosis0.013496459
Mean3.2043478
Median Absolute Deviation (MAD)0
Skewness1.1616176
Sum2211
Variance8.7665994
MonotonicityNot monotonic
2025-01-05T16:04:55.592816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 347
50.3%
2 59
 
8.6%
3 56
 
8.1%
10 56
 
8.1%
4 44
 
6.4%
5 33
 
4.8%
7 30
 
4.3%
6 30
 
4.3%
8 28
 
4.1%
9 7
 
1.0%
ValueCountFrequency (%)
1 347
50.3%
2 59
 
8.6%
3 56
 
8.1%
4 44
 
6.4%
5 33
 
4.8%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.1%
9 7
 
1.0%
10 56
 
8.1%
ValueCountFrequency (%)
10 56
 
8.1%
9 7
 
1.0%
8 28
 
4.1%
7 30
 
4.3%
6 30
 
4.3%
5 33
 
4.8%
4 44
 
6.4%
3 56
 
8.1%
2 59
 
8.6%
1 347
50.3%

Marginal Adhesion
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8275362
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:55.694775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8677874
Coefficient of variation (CV)1.0142354
Kurtosis0.92519186
Mean2.8275362
Median Absolute Deviation (MAD)0
Skewness1.5054065
Sum1951
Variance8.2242044
MonotonicityNot monotonic
2025-01-05T16:04:55.790781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 400
58.0%
3 58
 
8.4%
2 56
 
8.1%
10 55
 
8.0%
4 33
 
4.8%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.2%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 400
58.0%
2 56
 
8.1%
3 58
 
8.4%
4 33
 
4.8%
5 23
 
3.3%
6 22
 
3.2%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
8.0%
ValueCountFrequency (%)
10 55
 
8.0%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.2%
5 23
 
3.3%
4 33
 
4.8%
3 58
 
8.4%
2 56
 
8.1%
1 400
58.0%

Single Epithelial Cell Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2130435
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:55.887051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2009638
Coefficient of variation (CV)0.68500904
Kurtosis2.2065476
Mean3.2130435
Median Absolute Deviation (MAD)0
Skewness1.7167799
Sum2217
Variance4.8442418
MonotonicityNot monotonic
2025-01-05T16:04:55.993210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 382
55.4%
3 71
 
10.3%
4 48
 
7.0%
1 45
 
6.5%
6 41
 
5.9%
5 39
 
5.7%
10 30
 
4.3%
8 20
 
2.9%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 45
 
6.5%
2 382
55.4%
3 71
 
10.3%
4 48
 
7.0%
5 39
 
5.7%
6 41
 
5.9%
7 12
 
1.7%
8 20
 
2.9%
9 2
 
0.3%
10 30
 
4.3%
ValueCountFrequency (%)
10 30
 
4.3%
9 2
 
0.3%
8 20
 
2.9%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.7%
4 48
 
7.0%
3 71
 
10.3%
2 382
55.4%
1 45
 
6.5%

Bare Nuclei
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5028986
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:56.091204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35.75
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation3.6227178
Coefficient of variation (CV)1.0342058
Kurtosis-0.74689471
Mean3.5028986
Median Absolute Deviation (MAD)0
Skewness1.0140652
Sum2417
Variance13.124084
MonotonicityNot monotonic
2025-01-05T16:04:56.189243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 410
59.4%
10 130
 
18.8%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.1%
8 22
 
3.2%
4 19
 
2.8%
9 9
 
1.3%
7 8
 
1.2%
6 4
 
0.6%
ValueCountFrequency (%)
1 410
59.4%
2 30
 
4.3%
3 28
 
4.1%
4 19
 
2.8%
5 30
 
4.3%
6 4
 
0.6%
7 8
 
1.2%
8 22
 
3.2%
9 9
 
1.3%
10 130
 
18.8%
ValueCountFrequency (%)
10 130
 
18.8%
9 9
 
1.3%
8 22
 
3.2%
7 8
 
1.2%
6 4
 
0.6%
5 30
 
4.3%
4 19
 
2.8%
3 28
 
4.1%
2 30
 
4.3%
1 410
59.4%

Bland Chromatin
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4362319
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:56.285207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4440604
Coefficient of variation (CV)0.71126179
Kurtosis0.1844237
Mean3.4362319
Median Absolute Deviation (MAD)1
Skewness1.1012658
Sum2371
Variance5.9734314
MonotonicityNot monotonic
2025-01-05T16:04:56.383656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 165
23.9%
3 160
23.2%
1 151
21.9%
7 71
10.3%
4 40
 
5.8%
5 34
 
4.9%
8 28
 
4.1%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 151
21.9%
2 165
23.9%
3 160
23.2%
4 40
 
5.8%
5 34
 
4.9%
6 10
 
1.4%
7 71
10.3%
8 28
 
4.1%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.1%
7 71
10.3%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.8%
3 160
23.2%
2 165
23.9%
1 151
21.9%

Normal Nucleoli
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8855072
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:56.478653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0676823
Coefficient of variation (CV)1.0631345
Kurtosis0.419946
Mean2.8855072
Median Absolute Deviation (MAD)0
Skewness1.4052866
Sum1991
Variance9.410675
MonotonicityNot monotonic
2025-01-05T16:04:56.571750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 436
63.2%
10 61
 
8.8%
3 42
 
6.1%
2 36
 
5.2%
8 24
 
3.5%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 436
63.2%
2 36
 
5.2%
3 42
 
6.1%
4 18
 
2.6%
5 19
 
2.8%
6 22
 
3.2%
7 16
 
2.3%
8 24
 
3.5%
9 16
 
2.3%
10 61
 
8.8%
ValueCountFrequency (%)
10 61
 
8.8%
9 16
 
2.3%
8 24
 
3.5%
7 16
 
2.3%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
3 42
 
6.1%
2 36
 
5.2%
1 436
63.2%

Mitoses
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5942029
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-05T16:04:56.666024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7242305
Coefficient of variation (CV)1.0815627
Kurtosis12.489306
Mean1.5942029
Median Absolute Deviation (MAD)0
Skewness3.5414741
Sum1100
Variance2.9729707
MonotonicityNot monotonic
2025-01-05T16:04:56.759242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 571
82.8%
2 35
 
5.1%
3 32
 
4.6%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.2%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 571
82.8%
2 35
 
5.1%
3 32
 
4.6%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.2%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.2%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 32
 
4.6%
2 35
 
5.1%
1 571
82.8%

Class
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
benign
452 
malignant
238 

Length

Max length9
Median length6
Mean length7.0347826
Min length6

Characters and Unicode

Total characters4854
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbenign
2nd rowbenign
3rd rowbenign
4th rowbenign
5th rowbenign

Common Values

ValueCountFrequency (%)
benign 452
65.5%
malignant 238
34.5%

Length

2025-01-05T16:04:56.867422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T16:04:56.977274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
benign 452
65.5%
malignant 238
34.5%

Most occurring characters

ValueCountFrequency (%)
n 1380
28.4%
g 690
14.2%
i 690
14.2%
a 476
 
9.8%
b 452
 
9.3%
e 452
 
9.3%
m 238
 
4.9%
l 238
 
4.9%
t 238
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4854
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1380
28.4%
g 690
14.2%
i 690
14.2%
a 476
 
9.8%
b 452
 
9.3%
e 452
 
9.3%
m 238
 
4.9%
l 238
 
4.9%
t 238
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4854
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1380
28.4%
g 690
14.2%
i 690
14.2%
a 476
 
9.8%
b 452
 
9.3%
e 452
 
9.3%
m 238
 
4.9%
l 238
 
4.9%
t 238
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1380
28.4%
g 690
14.2%
i 690
14.2%
a 476
 
9.8%
b 452
 
9.3%
e 452
 
9.3%
m 238
 
4.9%
l 238
 
4.9%
t 238
 
4.9%

Interactions

2025-01-05T16:04:53.801603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:47.605487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.424270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.302743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.053840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.797268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.553977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.302339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.046874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.890571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:47.739154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.513445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.384789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.135929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.883461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.635149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.387148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.130634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.983577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:47.827552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.719450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.467752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.220144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.966330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.719317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.468762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.215778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.073608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:47.908295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.802430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.550373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.301727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.054380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.799694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.555723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.297782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.163610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:47.990381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.884523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.632371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.385722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.137337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.883813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.634999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.380883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.247573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.074679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.967484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.723845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.468688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.221376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.968814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.718960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.462846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.326535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.157275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.053523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.804820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.549720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.304344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.049850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.800997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.543885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.411568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.244306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.135490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.889857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.632017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.387347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.141404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.882959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.632882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:54.492582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:48.334267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.221520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:49.969840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:50.716352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:51.468761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.223626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:52.965838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-05T16:04:53.715308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-05T16:04:57.048668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Bare NucleiBland ChromatinClassClump ThicknessMarginal AdhesionMitosesNormal NucleoliSingle Epithelial Cell SizeUniformity of Cell ShapeUniformity of Cell Size
Bare Nuclei1.0000.6750.8400.5920.6900.4750.6560.6820.7440.764
Bland Chromatin0.6751.0000.8040.5390.6270.3860.6660.6420.6940.721
Class0.8400.8041.0000.7390.7420.5190.7680.7890.8590.874
Clump Thickness0.5920.5390.7391.0000.5430.4180.5680.5800.6640.665
Marginal Adhesion0.6900.6270.7420.5431.0000.4460.6350.6710.7150.746
Mitoses0.4750.3860.5190.4180.4461.0000.5030.4800.4720.508
Normal Nucleoli0.6560.6660.7680.5680.6350.5031.0000.7060.7250.757
Single Epithelial Cell Size0.6820.6420.7890.5800.6710.4800.7061.0000.7570.785
Uniformity of Cell Shape0.7440.6940.8590.6640.7150.4720.7250.7571.0000.891
Uniformity of Cell Size0.7640.7210.8740.6650.7460.5080.7570.7850.8911.000

Missing values

2025-01-05T16:04:54.738270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-05T16:04:54.914506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Clump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
0511121311benign
15445710321benign
2311122311benign
3688134371benign
4411321311benign
5810108710971malignant
61111210311benign
7212121311benign
8211121115benign
9421121211benign
Clump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
680111121118benign
681111321111benign
68251010545441malignant
683311121111benign
684311121212benign
685311132111benign
686211121111benign
687510103738102malignant
6884864341061malignant
6894885451041malignant

Duplicate rows

Most frequently occurring

Clump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass# duplicates
3111121111benign26
5111121311benign23
4111121211benign22
19311121211benign20
18311121111benign12
12211121111benign10
20311121311benign10
25411121111benign10
26411121211benign10
34511121211benign10